Overview

Brought to you by YData

Dataset statistics

Number of variables26
Number of observations101693
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory20.2 MiB
Average record size in memory208.0 B

Variable types

Numeric19
Categorical4
Text2
DateTime1

Alerts

Average_Rating is highly overall correlated with Hotel_Name and 6 other fieldsHigh correlation
Crawled_date is highly overall correlated with Hotel_Name and 1 other fieldsHigh correlation
FOG Index is highly overall correlated with Flesch Reading EaseHigh correlation
Flesch Reading Ease is highly overall correlated with FOG IndexHigh correlation
Hotel_Name is highly overall correlated with Average_Rating and 10 other fieldsHigh correlation
Num_of_Ratings is highly overall correlated with Hotel_Name and 1 other fieldsHigh correlation
Unnamed: 0 is highly overall correlated with Hotel_Name and 1 other fieldsHigh correlation
breadth is highly overall correlated with depth and 1 other fieldsHigh correlation
cleanliness_score is highly overall correlated with Average_Rating and 6 other fieldsHigh correlation
comfort_score is highly overall correlated with Average_Rating and 5 other fieldsHigh correlation
depth is highly overall correlated with breadthHigh correlation
employee_friendliness_score is highly overall correlated with Average_Rating and 5 other fieldsHigh correlation
facility_score is highly overall correlated with Average_Rating and 6 other fieldsHigh correlation
hotel_grade is highly overall correlated with Average_Rating and 5 other fieldsHigh correlation
location_score is highly overall correlated with Hotel_NameHigh correlation
text_length is highly overall correlated with breadthHigh correlation
value_for_money_score is highly overall correlated with Average_Rating and 6 other fieldsHigh correlation
is_photo is highly imbalanced (70.6%) Imbalance
Crawled_date is highly imbalanced (83.3%) Imbalance
Unnamed: 0 is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
Review_Text has unique values Unique
Helpfulness has 92031 (90.5%) zeros Zeros
Deviation of star ratings has 2537 (2.5%) zeros Zeros

Reproduction

Analysis started2025-02-05 07:49:19.917456
Analysis finished2025-02-05 07:49:58.302897
Duration38.39 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct101693
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51631.052
Minimum0
Maximum103563
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size794.6 KiB
2025-02-05T16:49:58.372088image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5122.6
Q125688
median51615
Q377443
95-th percentile98361.4
Maximum103563
Range103563
Interquartile range (IQR)51755

Descriptive statistics

Standard deviation29900.729
Coefficient of variation (CV)0.57912299
Kurtosis-1.1999338
Mean51631.052
Median Absolute Deviation (MAD)25878
Skewness0.0043273899
Sum5.2505166 × 109
Variance8.9405362 × 108
MonotonicityStrictly increasing
2025-02-05T16:49:58.472683image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
68874 1
 
< 0.1%
68872 1
 
< 0.1%
68871 1
 
< 0.1%
68870 1
 
< 0.1%
68869 1
 
< 0.1%
68868 1
 
< 0.1%
68867 1
 
< 0.1%
68866 1
 
< 0.1%
68865 1
 
< 0.1%
Other values (101683) 101683
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
103563 1
< 0.1%
103562 1
< 0.1%
103561 1
< 0.1%
103560 1
< 0.1%
103559 1
< 0.1%
103558 1
< 0.1%
103557 1
< 0.1%
103556 1
< 0.1%
103555 1
< 0.1%
103553 1
< 0.1%

Hotel_Name
Categorical

High correlation 

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size794.6 KiB
zedwell-trocaderor
 
4937
lancaster-gate
 
4887
thistletower
 
4878
stgileshotel
 
4866
z-trafalgar
 
4655
Other values (28)
77470 

Length

Max length35
Median length22
Mean length15.721112
Min length3

Characters and Unicode

Total characters1598727
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowstudios2let
2nd rowstudios2let
3rd rowstudios2let
4th rowstudios2let
5th rowstudios2let

Common Values

ValueCountFrequency (%)
zedwell-trocaderor 4937
 
4.9%
lancaster-gate 4887
 
4.8%
thistletower 4878
 
4.8%
stgileshotel 4866
 
4.8%
z-trafalgar 4655
 
4.6%
nyx-hotel-london-by-leonardo-hotels 4487
 
4.4%
radissonblugrafton 3675
 
3.6%
cityinnwestminster 3630
 
3.6%
sidneyhotel 3510
 
3.5%
ace 3476
 
3.4%
Other values (23) 58692
57.7%

Length

2025-02-05T16:49:58.570503image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
zedwell-trocaderor 4937
 
4.9%
lancaster-gate 4887
 
4.8%
thistletower 4878
 
4.8%
stgileshotel 4866
 
4.8%
z-trafalgar 4655
 
4.6%
nyx-hotel-london-by-leonardo-hotels 4487
 
4.4%
radissonblugrafton 3675
 
3.6%
cityinnwestminster 3630
 
3.6%
sidneyhotel 3510
 
3.5%
ace 3476
 
3.4%
Other values (23) 58692
57.7%

Most occurring characters

ValueCountFrequency (%)
e 171730
10.7%
o 165628
10.4%
t 164754
10.3%
l 139017
 
8.7%
a 116689
 
7.3%
r 109509
 
6.8%
n 99081
 
6.2%
s 89360
 
5.6%
- 80136
 
5.0%
h 72689
 
4.5%
Other values (16) 390134
24.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1598727
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 171730
10.7%
o 165628
10.4%
t 164754
10.3%
l 139017
 
8.7%
a 116689
 
7.3%
r 109509
 
6.8%
n 99081
 
6.2%
s 89360
 
5.6%
- 80136
 
5.0%
h 72689
 
4.5%
Other values (16) 390134
24.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1598727
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 171730
10.7%
o 165628
10.4%
t 164754
10.3%
l 139017
 
8.7%
a 116689
 
7.3%
r 109509
 
6.8%
n 99081
 
6.2%
s 89360
 
5.6%
- 80136
 
5.0%
h 72689
 
4.5%
Other values (16) 390134
24.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1598727
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 171730
10.7%
o 165628
10.4%
t 164754
10.3%
l 139017
 
8.7%
a 116689
 
7.3%
r 109509
 
6.8%
n 99081
 
6.2%
s 89360
 
5.6%
- 80136
 
5.0%
h 72689
 
4.5%
Other values (16) 390134
24.4%

Review_Text
Text

Unique 

Distinct101693
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size794.6 KiB
2025-02-05T16:49:58.834510image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3604
Median length1901
Mean length211.96352
Min length1

Characters and Unicode

Total characters21555206
Distinct characters2192
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique101693 ?
Unique (%)100.0%

Sample

1st rowPerfect location with good connections and shops and pubs
2nd rowThe room had everything you needed. Near to amenities, was good room for price just needs little updatingThe bed was so hard it felt like sleeping on a hard floor, you had to make sure you had something on your feet as flooring pinched you feet needs changing
3rd rowConveniently nearby St. Pancras, very small but clean and pleasant room (first floor with small balcony to street side). Interesting area.Luggage service can be improved by offering to lock luggage up instead of it just being put into the hall with all risks on the guests.
4th rowReception staffed 24 hours a day.All good.
5th rowVery convenient to King’s Cross and the cityA little dated could do with a lick of paint
ValueCountFrequency (%)
the 195386
 
5.2%
and 135249
 
3.6%
was 114450
 
3.0%
to 88001
 
2.3%
a 85622
 
2.3%
room 65652
 
1.7%
in 57965
 
1.5%
very 49945
 
1.3%
for 46262
 
1.2%
location 44905
 
1.2%
Other values (112839) 2881166
76.5%
2025-02-05T16:49:59.216170image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3659420
17.0%
e 2033083
 
9.4%
o 1529032
 
7.1%
t 1490307
 
6.9%
a 1473688
 
6.8%
n 1137142
 
5.3%
r 1033282
 
4.8%
i 1025785
 
4.8%
s 958874
 
4.4%
l 818520
 
3.8%
Other values (2182) 6396073
29.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21555206
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3659420
17.0%
e 2033083
 
9.4%
o 1529032
 
7.1%
t 1490307
 
6.9%
a 1473688
 
6.8%
n 1137142
 
5.3%
r 1033282
 
4.8%
i 1025785
 
4.8%
s 958874
 
4.4%
l 818520
 
3.8%
Other values (2182) 6396073
29.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21555206
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3659420
17.0%
e 2033083
 
9.4%
o 1529032
 
7.1%
t 1490307
 
6.9%
a 1473688
 
6.8%
n 1137142
 
5.3%
r 1033282
 
4.8%
i 1025785
 
4.8%
s 958874
 
4.4%
l 818520
 
3.8%
Other values (2182) 6396073
29.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21555206
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3659420
17.0%
e 2033083
 
9.4%
o 1529032
 
7.1%
t 1490307
 
6.9%
a 1473688
 
6.8%
n 1137142
 
5.3%
r 1033282
 
4.8%
i 1025785
 
4.8%
s 958874
 
4.4%
l 818520
 
3.8%
Other values (2182) 6396073
29.7%
Distinct1111
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size794.6 KiB
Minimum2021-12-01 00:00:00
Maximum2024-12-16 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-05T16:49:59.316684image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:59.416948image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Rating
Real number (ℝ)

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7064105
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size794.6 KiB
2025-02-05T16:49:59.501766image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q17
median8
Q39
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9083027
Coefficient of variation (CV)0.24762536
Kurtosis2.0209368
Mean7.7064105
Median Absolute Deviation (MAD)1
Skewness-1.2941539
Sum783688
Variance3.6416191
MonotonicityNot monotonic
2025-02-05T16:49:59.581994image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
8 29854
29.4%
9 19098
18.8%
7 17189
16.9%
10 16699
16.4%
6 7238
 
7.1%
5 4496
 
4.4%
4 2487
 
2.4%
3 1867
 
1.8%
1 1774
 
1.7%
2 917
 
0.9%
Other values (15) 74
 
0.1%
ValueCountFrequency (%)
1 1774
 
1.7%
2 917
 
0.9%
2.5 1
 
< 0.1%
2.9 1
 
< 0.1%
3 1867
1.8%
3.8 1
 
< 0.1%
4 2487
2.4%
4.6 1
 
< 0.1%
5 4496
4.4%
5.4 2
 
< 0.1%
ValueCountFrequency (%)
10 16699
16.4%
9.6 15
 
< 0.1%
9.2 12
 
< 0.1%
9 19098
18.8%
8.8 8
 
< 0.1%
8.3 7
 
< 0.1%
8 29854
29.4%
7.9 9
 
< 0.1%
7.5 5
 
< 0.1%
7.1 3
 
< 0.1%

Average_Rating
Real number (ℝ)

High correlation 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.843059
Minimum7
Maximum8.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size794.6 KiB
2025-02-05T16:49:59.654817image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7
Q17.7
median7.8
Q38.2
95-th percentile8.6
Maximum8.7
Range1.7
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.42189297
Coefficient of variation (CV)0.05379189
Kurtosis-0.32844262
Mean7.843059
Median Absolute Deviation (MAD)0.2
Skewness0.027417095
Sum797584.2
Variance0.17799368
MonotonicityNot monotonic
2025-02-05T16:49:59.734899image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
7.7 24861
24.4%
7.8 10060
9.9%
7.9 9511
 
9.4%
8.4 9142
 
9.0%
7.4 7762
 
7.6%
7 6849
 
6.7%
8.3 6557
 
6.4%
7.6 5503
 
5.4%
8.6 4746
 
4.7%
8 4324
 
4.3%
Other values (5) 12378
12.2%
ValueCountFrequency (%)
7 6849
 
6.7%
7.1 2042
 
2.0%
7.4 7762
 
7.6%
7.5 2159
 
2.1%
7.6 5503
 
5.4%
7.7 24861
24.4%
7.8 10060
9.9%
7.9 9511
 
9.4%
8 4324
 
4.3%
8.1 3046
 
3.0%
ValueCountFrequency (%)
8.7 2568
 
2.5%
8.6 4746
 
4.7%
8.4 9142
 
9.0%
8.3 6557
 
6.4%
8.2 2563
 
2.5%
8.1 3046
 
3.0%
8 4324
 
4.3%
7.9 9511
 
9.4%
7.8 10060
9.9%
7.7 24861
24.4%

Num_of_Ratings
Real number (ℝ)

High correlation 

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11495.815
Minimum5613
Maximum39497
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size794.6 KiB
2025-02-05T16:49:59.818944image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum5613
5-th percentile5898
Q16515
median9382
Q313923
95-th percentile20956
Maximum39497
Range33884
Interquartile range (IQR)7408

Descriptive statistics

Standard deviation7416.8887
Coefficient of variation (CV)0.64518163
Kurtosis7.0863642
Mean11495.815
Median Absolute Deviation (MAD)3105
Skewness2.5945932
Sum1.1690439 × 109
Variance55010238
MonotonicityNot monotonic
2025-02-05T16:49:59.907437image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
39497 4937
 
4.9%
14445 4887
 
4.8%
20956 4878
 
4.8%
14989 4866
 
4.8%
13923 4655
 
4.6%
9394 4487
 
4.4%
9315 3675
 
3.6%
7467 3630
 
3.6%
12641 3510
 
3.5%
6277 3476
 
3.4%
Other values (23) 58692
57.7%
ValueCountFrequency (%)
5613 1895
1.9%
5715 1960
1.9%
5898 2563
2.5%
5932 3006
3.0%
5933 2953
2.9%
6120 2033
2.0%
6248 2502
2.5%
6277 3476
3.4%
6335 2127
2.1%
6404 1983
1.9%
ValueCountFrequency (%)
39497 4937
4.9%
20956 4878
4.8%
15320 2825
2.8%
14989 4866
4.8%
14445 4887
4.8%
13923 4655
4.6%
12641 3510
3.5%
12340 2927
2.9%
11670 3470
3.4%
11045 2786
2.7%

Helpfulness
Real number (ℝ)

Zeros 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11225945
Minimum0
Maximum14
Zeros92031
Zeros (%)90.5%
Negative0
Negative (%)0.0%
Memory size794.6 KiB
2025-02-05T16:50:00.073002image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum14
Range14
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.38674756
Coefficient of variation (CV)3.4451226
Kurtosis58.132625
Mean0.11225945
Median Absolute Deviation (MAD)0
Skewness5.2864687
Sum11416
Variance0.14957367
MonotonicityNot monotonic
2025-02-05T16:50:00.146431image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 92031
90.5%
1 8340
 
8.2%
2 1050
 
1.0%
3 192
 
0.2%
4 42
 
< 0.1%
5 21
 
< 0.1%
6 9
 
< 0.1%
10 2
 
< 0.1%
7 2
 
< 0.1%
8 2
 
< 0.1%
Other values (2) 2
 
< 0.1%
ValueCountFrequency (%)
0 92031
90.5%
1 8340
 
8.2%
2 1050
 
1.0%
3 192
 
0.2%
4 42
 
< 0.1%
5 21
 
< 0.1%
6 9
 
< 0.1%
7 2
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
14 1
 
< 0.1%
10 2
 
< 0.1%
9 1
 
< 0.1%
8 2
 
< 0.1%
7 2
 
< 0.1%
6 9
 
< 0.1%
5 21
 
< 0.1%
4 42
 
< 0.1%
3 192
 
0.2%
2 1050
1.0%

is_photo
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size794.6 KiB
0
96432 
1
 
5261

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters101693
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 96432
94.8%
1 5261
 
5.2%

Length

2025-02-05T16:50:00.228844image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-05T16:50:00.300101image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 96432
94.8%
1 5261
 
5.2%

Most occurring characters

ValueCountFrequency (%)
0 96432
94.8%
1 5261
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 101693
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 96432
94.8%
1 5261
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 101693
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 96432
94.8%
1 5261
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 101693
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 96432
94.8%
1 5261
 
5.2%
Distinct58013
Distinct (%)57.0%
Missing0
Missing (%)0.0%
Memory size794.6 KiB
2025-02-05T16:50:00.499216image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length120
Median length104
Mean length30.736147
Min length1

Characters and Unicode

Total characters3125651
Distinct characters853
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55598 ?
Unique (%)54.7%

Sample

1st rowExceptional
2nd rowVery good
3rd rowConvenient location
4th rowPeaceful position in an elegant street close to 3 major stations and the Bloomsbury area.
5th rowGreat little gem in the city centre
ValueCountFrequency (%)
good 27150
 
5.0%
location 17475
 
3.2%
very 17180
 
3.2%
and 17051
 
3.2%
great 15107
 
2.8%
stay 14641
 
2.7%
a 14223
 
2.6%
for 13078
 
2.4%
the 12811
 
2.4%
hotel 12270
 
2.3%
Other values (16009) 377523
70.1%
2025-02-05T16:50:00.832586image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
438406
14.0%
e 292527
 
9.4%
o 268475
 
8.6%
a 226999
 
7.3%
t 223340
 
7.1%
n 175424
 
5.6%
r 151181
 
4.8%
l 147815
 
4.7%
i 144651
 
4.6%
s 109867
 
3.5%
Other values (843) 946966
30.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3125651
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
438406
14.0%
e 292527
 
9.4%
o 268475
 
8.6%
a 226999
 
7.3%
t 223340
 
7.1%
n 175424
 
5.6%
r 151181
 
4.8%
l 147815
 
4.7%
i 144651
 
4.6%
s 109867
 
3.5%
Other values (843) 946966
30.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3125651
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
438406
14.0%
e 292527
 
9.4%
o 268475
 
8.6%
a 226999
 
7.3%
t 223340
 
7.1%
n 175424
 
5.6%
r 151181
 
4.8%
l 147815
 
4.7%
i 144651
 
4.6%
s 109867
 
3.5%
Other values (843) 946966
30.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3125651
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
438406
14.0%
e 292527
 
9.4%
o 268475
 
8.6%
a 226999
 
7.3%
t 223340
 
7.1%
n 175424
 
5.6%
r 151181
 
4.8%
l 147815
 
4.7%
i 144651
 
4.6%
s 109867
 
3.5%
Other values (843) 946966
30.3%

hotel_grade
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size794.6 KiB
4
45517 
3
41258 
5
7828 
0
4937 
2
 
2153

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters101693
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
4 45517
44.8%
3 41258
40.6%
5 7828
 
7.7%
0 4937
 
4.9%
2 2153
 
2.1%

Length

2025-02-05T16:50:00.918696image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-05T16:50:00.988187image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
4 45517
44.8%
3 41258
40.6%
5 7828
 
7.7%
0 4937
 
4.9%
2 2153
 
2.1%

Most occurring characters

ValueCountFrequency (%)
4 45517
44.8%
3 41258
40.6%
5 7828
 
7.7%
0 4937
 
4.9%
2 2153
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 101693
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 45517
44.8%
3 41258
40.6%
5 7828
 
7.7%
0 4937
 
4.9%
2 2153
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 101693
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 45517
44.8%
3 41258
40.6%
5 7828
 
7.7%
0 4937
 
4.9%
2 2153
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 101693
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 45517
44.8%
3 41258
40.6%
5 7828
 
7.7%
0 4937
 
4.9%
2 2153
 
2.1%

employee_friendliness_score
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5473327
Minimum7.5
Maximum9.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size794.6 KiB
2025-02-05T16:50:01.056457image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum7.5
5-th percentile8
Q18.4
median8.6
Q38.7
95-th percentile9.1
Maximum9.1
Range1.6
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.36326641
Coefficient of variation (CV)0.042500558
Kurtosis0.82320249
Mean8.5473327
Median Absolute Deviation (MAD)0.2
Skewness-0.76187002
Sum869203.9
Variance0.13196248
MonotonicityNot monotonic
2025-02-05T16:50:01.134551image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
8.7 20691
20.3%
8.6 14473
14.2%
8.4 10387
10.2%
8.1 9803
9.6%
9.1 9183
9.0%
8.5 8186
 
8.0%
9 7851
 
7.7%
8.8 6513
 
6.4%
8.3 5629
 
5.5%
7.5 4025
 
4.0%
Other values (2) 4952
 
4.9%
ValueCountFrequency (%)
7.5 4025
 
4.0%
8 2825
 
2.8%
8.1 9803
9.6%
8.2 2127
 
2.1%
8.3 5629
 
5.5%
8.4 10387
10.2%
8.5 8186
 
8.0%
8.6 14473
14.2%
8.7 20691
20.3%
8.8 6513
 
6.4%
ValueCountFrequency (%)
9.1 9183
9.0%
9 7851
 
7.7%
8.8 6513
 
6.4%
8.7 20691
20.3%
8.6 14473
14.2%
8.5 8186
 
8.0%
8.4 10387
10.2%
8.3 5629
 
5.5%
8.2 2127
 
2.1%
8.1 9803
9.6%

facility_score
Real number (ℝ)

High correlation 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8512651
Minimum6.9
Maximum8.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size794.6 KiB
2025-02-05T16:50:01.210625image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum6.9
5-th percentile6.9
Q17.5
median7.8
Q38.3
95-th percentile8.7
Maximum8.7
Range1.8
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation0.49550699
Coefficient of variation (CV)0.063111739
Kurtosis-0.69441912
Mean7.8512651
Median Absolute Deviation (MAD)0.3
Skewness0.06281807
Sum798418.7
Variance0.24552718
MonotonicityNot monotonic
2025-02-05T16:50:01.294186image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
7.8 13816
13.6%
7.5 12549
12.3%
7.6 10037
9.9%
8.7 9841
9.7%
6.9 6849
 
6.7%
8.3 6013
 
5.9%
7.7 5978
 
5.9%
8 5872
 
5.8%
7.2 4937
 
4.9%
8.1 4690
 
4.6%
Other values (7) 21111
20.8%
ValueCountFrequency (%)
6.9 6849
6.7%
7.2 4937
 
4.9%
7.3 2042
 
2.0%
7.4 2825
 
2.8%
7.5 12549
12.3%
7.6 10037
9.9%
7.7 5978
5.9%
7.8 13816
13.6%
7.9 2953
 
2.9%
8 5872
5.8%
ValueCountFrequency (%)
8.7 9841
9.7%
8.6 1960
 
1.9%
8.5 3630
 
3.6%
8.4 4655
 
4.6%
8.3 6013
5.9%
8.2 3046
 
3.0%
8.1 4690
 
4.6%
8 5872
5.8%
7.9 2953
 
2.9%
7.8 13816
13.6%

cleanliness_score
Real number (ℝ)

High correlation 

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2549114
Minimum7.3
Maximum9.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size794.6 KiB
2025-02-05T16:50:01.372397image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum7.3
5-th percentile7.4
Q18
median8.2
Q38.7
95-th percentile8.8
Maximum9.1
Range1.8
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.4354913
Coefficient of variation (CV)0.052755418
Kurtosis-0.29176239
Mean8.2549114
Median Absolute Deviation (MAD)0.3
Skewness-0.18715622
Sum839466.7
Variance0.18965267
MonotonicityNot monotonic
2025-02-05T16:50:01.450824image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
8.7 13231
13.0%
8.2 11895
11.7%
8.8 10903
10.7%
8.1 10541
10.4%
7.9 10440
10.3%
8 9929
9.8%
8.3 8660
8.5%
8.4 7723
7.6%
7.3 4866
 
4.8%
9.1 4528
 
4.5%
Other values (4) 8977
8.8%
ValueCountFrequency (%)
7.3 4866
4.8%
7.4 1983
 
1.9%
7.5 2042
 
2.0%
7.8 2825
 
2.8%
7.9 10440
10.3%
8 9929
9.8%
8.1 10541
10.4%
8.2 11895
11.7%
8.3 8660
8.5%
8.4 7723
7.6%
ValueCountFrequency (%)
9.1 4528
 
4.5%
8.8 10903
10.7%
8.7 13231
13.0%
8.5 2127
 
2.1%
8.4 7723
7.6%
8.3 8660
8.5%
8.2 11895
11.7%
8.1 10541
10.4%
8 9929
9.8%
7.9 10440
10.3%

comfort_score
Real number (ℝ)

High correlation 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2442695
Minimum7.3
Maximum9.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size794.6 KiB
2025-02-05T16:50:01.526017image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum7.3
5-th percentile7.3
Q18
median8.2
Q38.7
95-th percentile8.9
Maximum9.1
Range1.8
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.4590495
Coefficient of variation (CV)0.055681039
Kurtosis-0.48364911
Mean8.2442695
Median Absolute Deviation (MAD)0.3
Skewness-0.11806461
Sum838384.5
Variance0.21072644
MonotonicityNot monotonic
2025-02-05T16:50:01.608691image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
8 16285
16.0%
8.2 10426
10.3%
7.9 9921
9.8%
8.8 9643
9.5%
8.1 9490
9.3%
8.9 7273
7.2%
8.3 6851
6.7%
7.3 6849
6.7%
8.5 6238
 
6.1%
8.7 4655
 
4.6%
Other values (6) 14062
13.8%
ValueCountFrequency (%)
7.3 6849
6.7%
7.4 2042
 
2.0%
7.8 3470
 
3.4%
7.9 9921
9.8%
8 16285
16.0%
8.1 9490
9.3%
8.2 10426
10.3%
8.3 6851
6.7%
8.4 1895
 
1.9%
8.5 6238
 
6.1%
ValueCountFrequency (%)
9.1 2568
 
2.5%
9 1960
 
1.9%
8.9 7273
7.2%
8.8 9643
9.5%
8.7 4655
4.6%
8.6 2127
 
2.1%
8.5 6238
6.1%
8.4 1895
 
1.9%
8.3 6851
6.7%
8.2 10426
10.3%

value_for_money_score
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7193278
Minimum7
Maximum8.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size794.6 KiB
2025-02-05T16:50:01.687916image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7.3
Q17.5
median7.7
Q37.9
95-th percentile8.2
Maximum8.3
Range1.3
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.32353898
Coefficient of variation (CV)0.041912843
Kurtosis-0.62953772
Mean7.7193278
Median Absolute Deviation (MAD)0.2
Skewness-0.18308805
Sum785001.6
Variance0.10467747
MonotonicityNot monotonic
2025-02-05T16:50:01.767519image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
7.9 24134
23.7%
7.4 13927
13.7%
7.5 12809
12.6%
7.7 9056
 
8.9%
8.1 8966
 
8.8%
7.6 8703
 
8.6%
8 5070
 
5.0%
7.3 4984
 
4.9%
7 4866
 
4.8%
8.2 4655
 
4.6%
ValueCountFrequency (%)
7 4866
 
4.8%
7.3 4984
 
4.9%
7.4 13927
13.7%
7.5 12809
12.6%
7.6 8703
 
8.6%
7.7 9056
 
8.9%
7.9 24134
23.7%
8 5070
 
5.0%
8.1 8966
 
8.8%
8.2 4655
 
4.6%
ValueCountFrequency (%)
8.3 4523
 
4.4%
8.2 4655
 
4.6%
8.1 8966
 
8.8%
8 5070
 
5.0%
7.9 24134
23.7%
7.7 9056
 
8.9%
7.6 8703
 
8.6%
7.5 12809
12.6%
7.4 13927
13.7%
7.3 4984
 
4.9%

location_score
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.1694423
Minimum8.2
Maximum9.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size794.6 KiB
2025-02-05T16:50:01.836936image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum8.2
5-th percentile8.6
Q19
median9.1
Q39.4
95-th percentile9.6
Maximum9.7
Range1.5
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.30996718
Coefficient of variation (CV)0.033804366
Kurtosis0.46670126
Mean9.1694423
Median Absolute Deviation (MAD)0.2
Skewness-0.51048551
Sum932468.1
Variance0.096079653
MonotonicityNot monotonic
2025-02-05T16:50:01.912616image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
8.9 16580
16.3%
9.1 15659
15.4%
9.4 12474
12.3%
9 11728
11.5%
9.3 11003
10.8%
9.5 7664
7.5%
9.6 7505
7.4%
9.2 6710
6.6%
8.6 5673
 
5.6%
9.7 4655
 
4.6%
ValueCountFrequency (%)
8.2 2042
 
2.0%
8.6 5673
 
5.6%
8.9 16580
16.3%
9 11728
11.5%
9.1 15659
15.4%
9.2 6710
6.6%
9.3 11003
10.8%
9.4 12474
12.3%
9.5 7664
7.5%
9.6 7505
7.4%
ValueCountFrequency (%)
9.7 4655
 
4.6%
9.6 7505
7.4%
9.5 7664
7.5%
9.4 12474
12.3%
9.3 11003
10.8%
9.2 6710
6.6%
9.1 15659
15.4%
9 11728
11.5%
8.9 16580
16.3%
8.6 5673
 
5.6%

Crawled_date
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size794.6 KiB
2024-12-02
99191 
2024-12-16
 
2502

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1016930
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-12-02
2nd row2024-12-02
3rd row2024-12-02
4th row2024-12-02
5th row2024-12-02

Common Values

ValueCountFrequency (%)
2024-12-02 99191
97.5%
2024-12-16 2502
 
2.5%

Length

2025-02-05T16:50:01.995265image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-05T16:50:02.059694image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
2024-12-02 99191
97.5%
2024-12-16 2502
 
2.5%

Most occurring characters

ValueCountFrequency (%)
2 404270
39.8%
- 203386
20.0%
0 200884
19.8%
1 104195
 
10.2%
4 101693
 
10.0%
6 2502
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1016930
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 404270
39.8%
- 203386
20.0%
0 200884
19.8%
1 104195
 
10.2%
4 101693
 
10.0%
6 2502
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1016930
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 404270
39.8%
- 203386
20.0%
0 200884
19.8%
1 104195
 
10.2%
4 101693
 
10.0%
6 2502
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1016930
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 404270
39.8%
- 203386
20.0%
0 200884
19.8%
1 104195
 
10.2%
4 101693
 
10.0%
6 2502
 
0.2%

title_length
Real number (ℝ)

Distinct30
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2954382
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size794.6 KiB
2025-02-05T16:50:02.126121image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median4
Q38
95-th percentile16
Maximum31
Range30
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.0035407
Coefficient of variation (CV)0.94487754
Kurtosis1.8423706
Mean5.2954382
Median Absolute Deviation (MAD)3
Skewness1.4666878
Sum538509
Variance25.035419
MonotonicityNot monotonic
2025-02-05T16:50:02.211869image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1 27471
27.0%
2 17294
17.0%
4 7031
 
6.9%
5 7021
 
6.9%
6 6528
 
6.4%
7 5387
 
5.3%
3 5345
 
5.3%
8 4403
 
4.3%
9 3564
 
3.5%
10 2888
 
2.8%
Other values (20) 14761
14.5%
ValueCountFrequency (%)
1 27471
27.0%
2 17294
17.0%
3 5345
 
5.3%
4 7031
 
6.9%
5 7021
 
6.9%
6 6528
 
6.4%
7 5387
 
5.3%
8 4403
 
4.3%
9 3564
 
3.5%
10 2888
 
2.8%
ValueCountFrequency (%)
31 1
 
< 0.1%
29 2
 
< 0.1%
28 7
 
< 0.1%
27 26
 
< 0.1%
26 49
 
< 0.1%
25 132
 
0.1%
24 250
0.2%
23 312
0.3%
22 405
0.4%
21 482
0.5%

text_length
Real number (ℝ)

High correlation 

Distinct424
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.019293
Minimum1
Maximum666
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size794.6 KiB
2025-02-05T16:50:02.303254image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q111
median24
Q348
95-th percentile113
Maximum666
Range665
Interquartile range (IQR)37

Descriptive statistics

Standard deviation41.234118
Coefficient of variation (CV)1.1138548
Kurtosis16.883715
Mean37.019293
Median Absolute Deviation (MAD)15
Skewness3.1942633
Sum3764603
Variance1700.2525
MonotonicityNot monotonic
2025-02-05T16:50:02.401369image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 2864
 
2.8%
5 2830
 
2.8%
7 2811
 
2.8%
9 2786
 
2.7%
8 2764
 
2.7%
10 2639
 
2.6%
4 2571
 
2.5%
12 2526
 
2.5%
11 2489
 
2.4%
14 2310
 
2.3%
Other values (414) 75103
73.9%
ValueCountFrequency (%)
1 756
 
0.7%
2 1375
1.4%
3 2050
2.0%
4 2571
2.5%
5 2830
2.8%
6 2864
2.8%
7 2811
2.8%
8 2764
2.7%
9 2786
2.7%
10 2639
2.6%
ValueCountFrequency (%)
666 1
< 0.1%
571 1
< 0.1%
568 1
< 0.1%
527 1
< 0.1%
510 1
< 0.1%
503 1
< 0.1%
493 1
< 0.1%
491 1
< 0.1%
479 1
< 0.1%
471 1
< 0.1%

time_lapsed
Real number (ℝ)

Distinct1098
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean540.84004
Minimum0
Maximum1097
Zeros74
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size794.6 KiB
2025-02-05T16:50:02.499507image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile58
Q1273
median532
Q3819
95-th percentile1020
Maximum1097
Range1097
Interquartile range (IQR)546

Descriptive statistics

Standard deviation310.48773
Coefficient of variation (CV)0.57408422
Kurtosis-1.1743369
Mean540.84004
Median Absolute Deviation (MAD)274
Skewness0.012506183
Sum54999646
Variance96402.632
MonotonicityNot monotonic
2025-02-05T16:50:02.595718image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
518 204
 
0.2%
511 203
 
0.2%
1008 187
 
0.2%
280 185
 
0.2%
882 183
 
0.2%
524 178
 
0.2%
910 178
 
0.2%
1001 175
 
0.2%
616 171
 
0.2%
1015 169
 
0.2%
Other values (1088) 99860
98.2%
ValueCountFrequency (%)
0 74
0.1%
1 102
0.1%
2 103
0.1%
3 103
0.1%
4 90
0.1%
5 68
0.1%
6 94
0.1%
7 142
0.1%
8 81
0.1%
9 67
0.1%
ValueCountFrequency (%)
1097 3
 
< 0.1%
1096 43
 
< 0.1%
1095 54
 
0.1%
1094 59
 
0.1%
1093 78
0.1%
1092 152
0.1%
1091 89
0.1%
1090 79
0.1%
1089 80
0.1%
1088 70
0.1%

Deviation of star ratings
Real number (ℝ)

Zeros 

Distinct104
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3356278
Minimum0
Maximum7.7
Zeros2537
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size794.6 KiB
2025-02-05T16:50:02.781845image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q10.4
median1
Q31.7
95-th percentile4
Maximum7.7
Range7.7
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.2913934
Coefficient of variation (CV)0.96688116
Kurtosis5.1357452
Mean1.3356278
Median Absolute Deviation (MAD)0.6
Skewness2.0643546
Sum135824
Variance1.6676968
MonotonicityNot monotonic
2025-02-05T16:50:02.888386image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6 7857
 
7.7%
0.4 7761
 
7.6%
0.3 7703
 
7.6%
1.4 5198
 
5.1%
1.3 4863
 
4.8%
0.7 4656
 
4.6%
1 4094
 
4.0%
0.1 4026
 
4.0%
1.6 3352
 
3.3%
2.3 3031
 
3.0%
Other values (94) 49152
48.3%
ValueCountFrequency (%)
0 2537
 
2.5%
0.1 4026
4.0%
0.2 922
 
0.9%
0.2 2860
 
2.8%
0.3 7703
7.6%
0.3 2451
 
2.4%
0.4 7761
7.6%
0.5 929
 
0.9%
0.6 7857
7.7%
0.6 2
 
< 0.1%
ValueCountFrequency (%)
7.7 17
 
< 0.1%
7.6 18
 
< 0.1%
7.4 110
 
0.1%
7.3 68
 
0.1%
7.2 8
 
< 0.1%
7.1 71
 
0.1%
7 93
 
0.1%
6.9 120
 
0.1%
6.8 175
 
0.2%
6.7 492
0.5%

FOG Index
Real number (ℝ)

High correlation 

Distinct2063
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.7008664
Minimum0
Maximum142.24
Zeros13
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size794.6 KiB
2025-02-05T16:50:02.987085image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.8
Q16.61
median8.57
Q311.6
95-th percentile18.84
Maximum142.24
Range142.24
Interquartile range (IQR)4.99

Descriptive statistics

Standard deviation5.3759318
Coefficient of variation (CV)0.55417027
Kurtosis18.798141
Mean9.7008664
Median Absolute Deviation (MAD)2.49
Skewness2.6341535
Sum986510.21
Variance28.900643
MonotonicityNot monotonic
2025-02-05T16:50:03.086564image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.04 2281
 
2.2%
10 2164
 
2.1%
9.07 1680
 
1.7%
8.51 1616
 
1.6%
11.6 1607
 
1.6%
8.2 1549
 
1.5%
8 1276
 
1.3%
14.53 1103
 
1.1%
13.2 959
 
0.9%
12 898
 
0.9%
Other values (2053) 86560
85.1%
ValueCountFrequency (%)
0 13
 
< 0.1%
0.4 344
0.3%
0.8 478
0.5%
1 18
 
< 0.1%
1.08 1
 
< 0.1%
1.2 716
0.7%
1.32 8
 
< 0.1%
1.4 56
 
0.1%
1.48 15
 
< 0.1%
1.52 6
 
< 0.1%
ValueCountFrequency (%)
142.24 1
 
< 0.1%
120.4 1
 
< 0.1%
106.39 1
 
< 0.1%
104.98 1
 
< 0.1%
86.1 1
 
< 0.1%
84.97 1
 
< 0.1%
80.4 5
< 0.1%
74.51 1
 
< 0.1%
72.32 1
 
< 0.1%
66 1
 
< 0.1%

Flesch Reading Ease
Real number (ℝ)

High correlation 

Distinct2435
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.499026
Minimum-555.59
Maximum206.84
Zeros0
Zeros (%)0.0%
Negative2710
Negative (%)2.7%
Memory size794.6 KiB
2025-02-05T16:50:03.185507image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-555.59
5-th percentile21.74
Q156.93
median71.44
Q381.02
95-th percentile93.81
Maximum206.84
Range762.43
Interquartile range (IQR)24.09

Descriptive statistics

Standard deviation29.164817
Coefficient of variation (CV)0.445271
Kurtosis36.970882
Mean65.499026
Median Absolute Deviation (MAD)11.21
Skewness-4.1086938
Sum6660792.5
Variance850.58653
MonotonicityNot monotonic
2025-02-05T16:50:03.285261image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68.77 845
 
0.8%
73.85 806
 
0.8%
79.26 796
 
0.8%
71.82 788
 
0.8%
81.29 785
 
0.8%
64.37 750
 
0.7%
56.93 747
 
0.7%
80.28 743
 
0.7%
78.25 709
 
0.7%
63.36 707
 
0.7%
Other values (2425) 94017
92.5%
ValueCountFrequency (%)
-555.59 2
 
< 0.1%
-470.99 3
 
< 0.1%
-386.39 5
 
< 0.1%
-301.79 41
 
< 0.1%
-265.85 1
 
< 0.1%
-260.5 1
 
< 0.1%
-219.22 1
 
< 0.1%
-218.2 5
 
< 0.1%
-217.19 104
0.1%
-177.93 1
 
< 0.1%
ValueCountFrequency (%)
206.84 13
 
< 0.1%
121.22 194
0.2%
120.21 171
0.2%
119.19 184
0.2%
118.68 1
 
< 0.1%
118.18 131
0.1%
117.77 1
 
< 0.1%
117.67 6
 
< 0.1%
117.26 1
 
< 0.1%
117.16 116
0.1%

depth
Real number (ℝ)

High correlation 

Distinct94036
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.51462251
Minimum9.9788716 × 10-18
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size794.6 KiB
2025-02-05T16:50:03.387891image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum9.9788716 × 10-18
5-th percentile0.039373492
Q10.38563247
median0.55302141
Q30.67136632
95-th percentile0.80328745
Maximum1
Range1
Interquartile range (IQR)0.28573385

Descriptive statistics

Standard deviation0.21836477
Coefficient of variation (CV)0.4243203
Kurtosis-0.0061038129
Mean0.51462251
Median Absolute Deviation (MAD)0.13414015
Skewness-0.56272271
Sum52333.507
Variance0.047683175
MonotonicityNot monotonic
2025-02-05T16:50:03.487853image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1702
 
1.7%
0.002382441358 243
 
0.2%
9.97887157 × 10-18128
 
0.1%
1.939563365 × 10-17104
 
0.1%
1.32685554 × 10-1797
 
0.1%
1.135575798 × 10-1783
 
0.1%
1.049586256 × 10-1777
 
0.1%
1.000506049 × 10-1773
 
0.1%
0.2829621402 72
 
0.1%
0.2522073769 66
 
0.1%
Other values (94026) 99048
97.4%
ValueCountFrequency (%)
9.97887157 × 10-18128
0.1%
1.000506049 × 10-1773
0.1%
1.049586256 × 10-1777
0.1%
1.077492818 × 10-171
 
< 0.1%
1.0910371 × 10-1741
 
< 0.1%
1.11725568 × 10-171
 
< 0.1%
1.120619813 × 10-171
 
< 0.1%
1.128725693 × 10-171
 
< 0.1%
1.135575798 × 10-1783
0.1%
1.135964433 × 10-171
 
< 0.1%
ValueCountFrequency (%)
1 1702
1.7%
0.9617011723 1
 
< 0.1%
0.9590542372 1
 
< 0.1%
0.9544693421 1
 
< 0.1%
0.9543592049 1
 
< 0.1%
0.9502031837 1
 
< 0.1%
0.9448245866 1
 
< 0.1%
0.9408650086 1
 
< 0.1%
0.9407216602 1
 
< 0.1%
0.9393919822 1
 
< 0.1%

breadth
Real number (ℝ)

High correlation 

Distinct93119
Distinct (%)91.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0915893
Minimum0.11451788
Maximum5.402044
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size794.6 KiB
2025-02-05T16:50:03.585125image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.11451788
5-th percentile0.92230132
Q11.4424259
median1.9107594
Q32.5383131
95-th percentile4.0986157
Maximum5.402044
Range5.2875261
Interquartile range (IQR)1.0958872

Descriptive statistics

Standard deviation0.94935818
Coefficient of variation (CV)0.45389321
Kurtosis1.1123125
Mean2.0915893
Median Absolute Deviation (MAD)0.52768523
Skewness0.96340279
Sum212699.99
Variance0.90128096
MonotonicityNot monotonic
2025-02-05T16:50:03.686143image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1145178764 1702
 
1.7%
4.514983221 458
 
0.5%
5.402044023 377
 
0.4%
3.784350471 243
 
0.2%
3.182034107 183
 
0.2%
4.053054492 128
 
0.1%
4.272895225 110
 
0.1%
3.671437901 97
 
0.1%
4.191737262 90
 
0.1%
4.471387918 83
 
0.1%
Other values (93109) 98222
96.6%
ValueCountFrequency (%)
0.1145178764 1702
1.7%
0.367216634 1
 
< 0.1%
0.3729368476 1
 
< 0.1%
0.3735259523 1
 
< 0.1%
0.3754088904 1
 
< 0.1%
0.3874966346 1
 
< 0.1%
0.3880055101 1
 
< 0.1%
0.3884958044 1
 
< 0.1%
0.3912099536 1
 
< 0.1%
0.3929158163 1
 
< 0.1%
ValueCountFrequency (%)
5.402044023 377
0.4%
5.402044023 4
 
< 0.1%
5.401914478 1
 
< 0.1%
5.40180921 1
 
< 0.1%
5.401558958 1
 
< 0.1%
5.401426672 1
 
< 0.1%
5.401373315 1
 
< 0.1%
5.401329444 1
 
< 0.1%
5.40128233 1
 
< 0.1%
5.401153131 1
 
< 0.1%

Interactions

2025-02-05T16:49:55.962793image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:27.970050image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:29.507317image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:30.962819image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:32.622732image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:34.246820image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:35.737579image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:37.342381image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:38.854993image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:40.460609image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:42.053248image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:43.549340image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:45.141668image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:46.624986image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:48.233480image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:49.677854image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:51.296406image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:52.889339image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:54.423088image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:56.036430image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:28.048842image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:29.580001image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:31.041992image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:32.709168image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:34.320953image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:35.814575image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:37.419853image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:39.017426image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:40.535149image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:42.129379image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:43.626316image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:45.216387image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:46.700361image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:48.305924image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:49.839018image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:51.372531image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:52.967326image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:54.495387image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:56.105117image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:28.120939image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:29.645920image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:31.119066image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:32.788513image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:34.394257image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:35.885915image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:37.492485image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:39.089765image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:40.609601image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:42.200143image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:43.698359image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:45.288137image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:46.770749image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:48.376596image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:49.911839image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:51.445414image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:53.040838image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:54.565619image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:56.184838image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:28.199400image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:29.724644image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:31.202461image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:32.877230image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:34.475020image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:35.969662image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:37.573587image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:39.172015image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:40.688774image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:42.281903image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:43.780619image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:45.368113image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:46.884216image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:48.455798image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:49.994659image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:51.527426image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:53.124331image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:54.644654image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:56.263742image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:28.280212image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:29.800334image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:31.290769image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:32.957559image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:34.556242image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:36.053142image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:37.654863image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:39.254618image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:40.772588image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:42.361614image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:43.862067image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:45.449458image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:46.965593image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:48.536351image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:50.079778image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:51.610054image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:53.207520image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:54.722265image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:56.340875image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:28.359187image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:29.876203image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:31.374444image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:33.038959image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:34.636008image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:36.133812image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:37.736045image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:39.334919image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:40.852065image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:42.441396image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:43.941582image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:45.528017image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:47.129124image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:48.612253image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:50.159863image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:51.691523image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:53.289901image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:54.800036image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:56.418913image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:28.437996image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:29.952682image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:31.540260image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:33.121501image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:34.717420image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:36.214972image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:37.818900image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:39.416938image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:40.935211image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:42.520970image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:44.022366image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:45.609054image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:47.210968image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:48.692326image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:50.244691image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:51.772971image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:53.374168image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:54.879174image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:56.497184image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:28.518005image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:30.031061image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:31.619753image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:33.204172image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:34.798217image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:36.297426image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:37.899334image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:39.498308image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:41.016225image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:42.602248image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:44.104318image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:45.689043image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:47.290289image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:48.770924image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:50.327299image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:51.856047image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:53.456155image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:55.045981image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:56.575989image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:28.596682image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:30.110147image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:31.700963image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:33.287567image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:34.878446image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:36.379601image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:37.979519image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:39.579707image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:41.096539image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:42.682329image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:44.185192image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:45.770922image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:47.373039image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:48.851142image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:50.411043image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:51.936583image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:53.540778image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:55.125596image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:56.655038image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:28.744780image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:30.196269image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:31.782350image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:33.369845image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:34.961361image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:36.460769image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:38.061643image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:39.662397image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:41.177496image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:42.764028image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:44.351691image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:45.851181image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:47.453424image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:48.928975image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:50.492543image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:52.019992image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:53.621914image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:55.204634image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:56.731315image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:28.820169image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:30.276041image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:31.862692image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:33.449410image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:35.039484image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:36.625807image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:38.142839image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:39.741414image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:41.256489image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:42.840072image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:44.431852image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:45.928621image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:47.533707image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:49.004896image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:50.575476image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:52.099128image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:53.704085image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:55.280269image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:56.811328image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:28.899218image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:30.358345image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:31.945593image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:33.531145image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:35.121049image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:36.706563image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:38.223504image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:39.825393image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:41.338403image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:42.920933image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:44.510941image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:46.007976image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:47.614118image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:49.082762image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:50.658936image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:52.182192image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:53.786269image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:55.358611image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:56.885836image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:28.974385image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:30.434854image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:32.028211image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:33.609028image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:35.199677image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:36.786221image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:38.303321image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:39.902344image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:41.417437image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:42.998470image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:44.589977image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:46.083612image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:47.690007image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:49.157754image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:50.738832image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:52.257320image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:53.865641image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:55.433748image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:56.961256image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:29.050026image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:30.510906image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:32.112113image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:33.687585image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:35.276442image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:36.866045image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:38.379864image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:39.983262image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:41.495744image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:43.075597image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:44.666760image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:46.160189image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:47.766677image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:49.230937image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:50.818458image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:52.422277image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:53.943494image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:55.508375image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:57.033005image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:29.123951image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:30.581565image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:32.190351image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:33.764189image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:35.351779image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:36.941285image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:38.457578image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:40.058107image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:41.570987image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:43.151573image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:44.743588image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:46.235633image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:47.841072image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:49.302046image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:50.895149image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:52.496174image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:54.020693image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:55.581210image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:57.113617image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:29.203549image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:30.661809image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:32.276329image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:33.848095image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:35.433062image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:37.025919image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:38.540116image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:40.144245image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:41.743244image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:43.233750image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:44.828957image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:46.317119image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:47.923948image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:49.380314image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:50.979672image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:52.579869image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:54.104733image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:55.661134image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:57.190194image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:29.283272image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:30.740983image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:32.364253image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:34.012017image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:35.513218image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:37.107810image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:38.622553image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:40.224761image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:41.822072image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:43.313641image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:44.909102image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:46.397217image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:48.003254image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:49.458885image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:51.060621image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:52.658633image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:54.187666image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:55.739491image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:57.268630image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:29.361652image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:30.821311image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:32.454630image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:34.095631image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:35.592175image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:37.189754image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:38.704052image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:40.307887image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:41.903724image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:43.397997image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:44.990779image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:46.476400image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:48.085025image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:49.535545image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:51.143345image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:52.739760image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:54.270155image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:55.818587image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:57.339736image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:29.435581image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:30.893131image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:32.539573image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:34.171224image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:35.666037image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:37.266482image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:38.780756image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:40.382953image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:41.979501image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:43.475394image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:45.065921image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:46.551674image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:48.159064image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:49.606817image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:51.219018image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:52.814888image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:54.346166image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-02-05T16:49:55.890588image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-02-05T16:50:03.776119image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Average_RatingCrawled_dateDeviation of star ratingsFOG IndexFlesch Reading EaseHelpfulnessHotel_NameNum_of_RatingsRatingUnnamed: 0breadthcleanliness_scorecomfort_scoredepthemployee_friendliness_scorefacility_scorehotel_gradeis_photolocation_scoretext_lengthtime_lapsedtitle_lengthvalue_for_money_score
Average_Rating1.0000.219-0.0130.007-0.047-0.0131.000-0.3040.276-0.0810.0400.9090.876-0.0210.8290.9390.5150.0660.151-0.0320.035-0.0020.694
Crawled_date0.2191.0000.0610.0070.0110.0001.0000.1890.0170.4790.0440.2980.3220.0380.3540.3230.1920.0010.3200.0130.0290.0090.693
Deviation of star ratings-0.0130.0611.000-0.0100.019-0.0430.183-0.065-0.0530.067-0.0140.0130.013-0.0170.0090.0200.1190.037-0.0950.047-0.015-0.0720.006
FOG Index0.0070.007-0.0101.000-0.751-0.0010.0160.0200.0100.0020.080-0.0000.004-0.0700.0060.0060.0100.0240.025-0.0890.0050.027-0.020
Flesch Reading Ease-0.0470.0110.019-0.7511.0000.0230.0300.009-0.1060.000-0.162-0.033-0.0330.127-0.049-0.0420.0140.026-0.0110.286-0.0020.012-0.008
Helpfulness-0.0130.000-0.043-0.0010.0231.0000.029-0.004-0.078-0.035-0.062-0.008-0.0070.041-0.030-0.0060.0110.025-0.0200.1200.0290.030-0.003
Hotel_Name1.0001.0000.1830.0160.0300.0291.0001.0000.1530.9360.1211.0001.0000.0961.0001.0001.0000.1291.0000.0220.1060.0271.000
Num_of_Ratings-0.3040.189-0.0650.0200.009-0.0041.0001.000-0.074-0.093-0.072-0.349-0.2210.064-0.319-0.3070.6040.1060.362-0.0010.0400.035-0.382
Rating0.2760.017-0.0530.010-0.106-0.0780.153-0.0741.000-0.0040.0510.2580.258-0.0090.2430.2650.1250.0660.064-0.187-0.001-0.0200.188
Unnamed: 0-0.0810.4790.0670.0020.000-0.0350.936-0.093-0.0041.000-0.011-0.021-0.0350.0240.116-0.0980.5540.0790.071-0.0080.0220.0080.086
breadth0.0400.044-0.0140.080-0.162-0.0620.121-0.0720.051-0.0111.0000.0210.001-0.7870.0390.0370.0340.064-0.051-0.535-0.022-0.1530.020
cleanliness_score0.9090.2980.013-0.000-0.033-0.0081.000-0.3490.258-0.0210.0211.0000.958-0.0030.8110.9450.5310.0690.093-0.0200.0280.0020.729
comfort_score0.8760.3220.0130.004-0.033-0.0071.000-0.2210.258-0.0350.0010.9581.0000.0180.7850.9380.4730.0580.117-0.0190.0580.0080.634
depth-0.0210.038-0.017-0.0700.1270.0410.0960.064-0.0090.024-0.787-0.0030.0181.000-0.012-0.0190.0270.0460.0500.4260.0350.133-0.005
employee_friendliness_score0.8290.3540.0090.006-0.049-0.0301.000-0.3190.2430.1160.0390.8110.785-0.0121.0000.7900.4260.0860.105-0.0320.0340.0000.664
facility_score0.9390.3230.0200.006-0.042-0.0061.000-0.3070.265-0.0980.0370.9450.938-0.0190.7901.0000.6530.0670.054-0.0260.030-0.0070.678
hotel_grade0.5150.1920.1190.0100.0140.0111.0000.6040.1250.5540.0340.5310.4730.0270.4260.6531.0000.0610.4330.0140.0550.0140.321
is_photo0.0660.0010.0370.0240.0260.0250.1290.1060.0660.0790.0640.0690.0580.0460.0860.0670.0611.0000.0530.0990.0110.0510.056
location_score0.1510.320-0.0950.025-0.011-0.0201.0000.3620.0640.071-0.0510.0930.1170.0500.1050.0540.4330.0531.000-0.000-0.0030.0550.028
text_length-0.0320.0130.047-0.0890.2860.1200.022-0.001-0.187-0.008-0.535-0.020-0.0190.426-0.032-0.0260.0140.099-0.0001.000-0.0280.223-0.013
time_lapsed0.0350.029-0.0150.005-0.0020.0290.1060.040-0.0010.022-0.0220.0280.0580.0350.0340.0300.0550.011-0.003-0.0281.000-0.0030.062
title_length-0.0020.009-0.0720.0270.0120.0300.0270.035-0.0200.008-0.1530.0020.0080.1330.000-0.0070.0140.0510.0550.223-0.0031.000-0.007
value_for_money_score0.6940.6930.006-0.020-0.008-0.0031.000-0.3820.1880.0860.0200.7290.634-0.0050.6640.6780.3210.0560.028-0.0130.062-0.0071.000

Missing values

2025-02-05T16:49:57.562852image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-05T16:49:57.915818image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0Hotel_NameReview_TextPosted_DateRatingAverage_RatingNum_of_RatingsHelpfulnessis_photoreview_titlehotel_gradeemployee_friendliness_scorefacility_scorecleanliness_scorecomfort_scorevalue_for_money_scorelocation_scoreCrawled_datetitle_lengthtext_lengthtime_lapsedDeviation of star ratingsFOG IndexFlesch Reading Easedepthbreadth
00studios2letPerfect location with good connections and shops and pubs2024-05-0110.07.61167000Exceptional38.37.57.97.87.69.32024-12-02192152.412.4962.340.4043192.930427
11studios2letThe room had everything you needed. Near to amenities, was good room for price just needs little updatingThe bed was so hard it felt like sleeping on a hard floor, you had to make sure you had something on your feet as flooring pinched you feet needs changing2024-12-028.07.61167000Very good38.37.57.97.87.69.32024-12-0224800.410.4380.960.5504831.213568
22studios2letConveniently nearby St. Pancras, very small but clean and pleasant room (first floor with small balcony to street side). Interesting area.Luggage service can be improved by offering to lock luggage up instead of it just being put into the hall with all risks on the guests.2024-12-018.07.61167000Convenient location38.37.57.97.87.69.32024-12-0224610.47.8672.870.5937001.601652
33studios2letReception staffed 24 hours a day.All good.2024-12-019.07.61167000Peaceful position in an elegant street close to 3 major stations and the Bloomsbury area.38.37.57.97.87.69.32024-12-0215711.48.5181.290.3430372.708736
44studios2letVery convenient to King’s Cross and the cityA little dated could do with a lick of paint2024-11-308.07.61167000Great little gem in the city centre38.37.57.97.87.69.32024-12-0271720.49.1588.060.7054261.030207
55studios2letLocated in a quiet area but close to Kings Cross station so getting around was easy. Several little pubs nearby for dining and some good coffee shops too.There is no lift so dragging a heavy suitcase up and down stairs was challenging. We had booked a room with terrace but the outdoor space was really minuscule - not what we had expected from the photos.2024-11-307.07.61167000Convenient, quiet location.38.37.57.97.87.69.32024-12-0236520.68.9072.160.4931651.671327
66studios2letIt's spacious, good value and so very quiet for London.You sometimes have to wriggle the loo flusher to stop it running and running2024-11-309.07.61167000Superb38.37.57.97.87.69.32024-12-0212321.44.6076.720.4406111.477398
77studios2letLocationLot of stairs (bad knee)2024-11-299.07.61167000Ideal location for travelling round38.37.57.97.87.69.32024-12-025531.410.0066.400.4127272.357962
88studios2letLocation was great, so near the stationWe were on the top floor, six flights of stairs and no lift.\nHeating was on 24:7 full temperature and no means of reducing it!2024-11-297.07.61167000Perfect location,38.37.57.97.87.69.32024-12-0223130.611.3681.120.5089561.917493
99studios2letThe location which is excellent for public transport and local dining. \nFriendly staffed reception where we could leave our travel bags all day after checking out.The climb up 3 flights of stairs was exhausting but it was our choice.\nIt was a small room and the kitchen facilities were very sparse ( but we didn't need them)2024-11-288.07.61167000Ideal accommodation for a short stay in London near St Pancreas station38.37.57.97.87.69.32024-12-02125740.49.1774.190.7787461.675657
Unnamed: 0Hotel_NameReview_TextPosted_DateRatingAverage_RatingNum_of_RatingsHelpfulnessis_photoreview_titlehotel_gradeemployee_friendliness_scorefacility_scorecleanliness_scorecomfort_scorevalue_for_money_scorelocation_scoreCrawled_datetitle_lengthtext_lengthtime_lapsedDeviation of star ratingsFOG IndexFlesch Reading Easedepthbreadth
101683103553montanahotelJust the locationVery poor facilities . Rooms are pretty old and the toilets are not functioning at the best . Breakfast was also very basic . If you need an English breakfast you need to upgrade and pay extra and it’s absolutely not worth it . Hash brown being the most basic was ridiculous. They really need to improve a lot . The only thing was staff was cooperative and helpful .2023-09-183.07.8624800나쁨39.07.78.28.28.09.42024-12-161724554.86.7970.390.6166481.496253
101684103555montanahotelIt was good enoughDelayed check in, cracked basin in bathroom, water pressure poor. Everything else was fine.2023-07-116.07.8624800Not a bad place to stay if it’s where you need to be.39.07.78.28.28.09.42024-12-1613175241.83.4079.770.5271462.300939
101685103556montanahotellocationhot room, shower didn’t drain, broken sink2023-07-015.07.8624800Great location and generally clean spot but the place is a bit a dated and the basement room was damp, hot and a bit mus39.07.78.28.28.09.42024-12-162575342.88.5138.990.1746632.642188
101686103557montanahotelGood to have tea/coffee and a fridge in the room.The building is beautiful but the interior decor leaves a lot to be desired. The hotel is Indian in style...red/gold...faded wallpaper and threadbare carpets...the room was OK but again needed updating. We were in the basement with no view...only rubbish out of the window. There was no hot breakfast so only cereals, fruit and pastries...but it was in a pleasant location...near the tube and shops/pubs etc...only a short walk to the Natural History museum.2023-04-256.07.8624800Lovely building with quite a grand entrance...let down by the interior...fine for overnight stay.39.07.78.28.28.09.42024-12-1614836011.86.3763.860.6810521.021631
101687103558montanahotellocationwater pressure was non existent.\ndespite several request to address the problem.2023-03-253.07.8624800while the staff was nice. They did very little to remedy the lack of shower and hot water problem we had .39.07.78.28.28.09.42024-12-1622126324.89.0723.090.5172362.098524
101688103559montanahotelConvenient and classy. The staff are excellent people, and Light of India is a fantastic restaurant. I would certainly stay again.N/A2022-12-2810.07.8624800Highly recommend this little gem situated in my favourite part of town.39.07.78.28.28.09.42024-12-1612217192.28.5164.370.6091161.975976
101689103560montanahotellovely atmosphere, extremely friendly and helpful staff.2022-07-0110.07.8624800Perfect location for our visit to the Royal Albert Hall and the Natural History Museum. would39.07.78.28.28.09.42024-12-161678992.214.2330.530.1878463.066338
101690103561montanahotelIt was a single room, a little small but it was fine for 1 person, it had everything I needed2022-06-2810.07.8624801The staff were very friendly and helpful. The position was perfect for sightseeing39.07.78.28.28.09.42024-12-1613209022.28.0076.560.3280682.343981
101691103562montanahotelVery clean and well maintained.The rooms are very nice and comfortable with staffs professionalism.The food are delicious,nice breakfast,lunch ,dinner and the cocktails are exceptional.Notting much just that there’s no parking.2022-02-1610.07.8624801Myself and my wife really enjoy our stay at this hotel,we love the service and all the staffs are amazing.Looking forwar39.07.78.28.28.09.42024-12-16213010342.28.3363.860.5356812.192362
101692103563montanahotelThe staff were very friendly and helpful! Especially Kampas The hotel was very clean and the fact that they had a wonderful Indian restaurant as part of it was amazing. Best Vindaloo ever!!!!Shower a tad small but adequate xx2022-02-0610.07.8624801Loved every minute! we will be back!! Xxx39.07.78.28.28.09.42024-12-1683910442.25.9769.990.5866101.649065